multi-agent-patterns

What this skill does

# Multi-Agent Architecture Patterns

Multi-agent architectures distribute work across multiple language model instances, each with its own context window. When designed well, this distribution enables capabilities beyond single-agent limits. When designed poorly, it introduces coordination overhead that negates benefits. The critical insight is that sub-agents exist primarily to isolate context, not to anthropomorphize role division.

## When to Activate

Activate this skill when:
- Single-agent context limits constrain task complexity
- Tasks decompose naturally into parallel subtasks
- Different subtasks require different tool sets or system prompts
- Building systems that must handle multiple domains simultaneously
- Scaling agent capabilities beyond single-context limits
- Designing production agent systems with multiple specialized components

## Core Concepts

Use multi-agent patterns when a single agent's context window cannot hold all task-relevant information. Context isolation is the primary benefit — each agent operates in a clean context without accumulated noise from other subtasks, preventing the telephone game problem where information degrades through repeated summarization.

Choose among three dominant patterns based on coordination needs, not organizational metaphor:

- **Supervisor/orchestrator** — Use for centralized control when tasks have clear decomposition and human oversight matters. A single coordinator delegates to specialists and synthesizes results.
- **Peer-to-peer/swarm** — Use for flexible exploration when rigid planning is counterproductive. Any agent can transfer control to any other through explicit handoff mechanisms.
- **Hierarchical** — Use for large-scale projects with layered abstraction (strategy, planning, execution). Each layer operates at a different level of detail with its own context structure.

Design every multi-agent system around explicit coordination protocols, consensus mechanisms that resist sycophancy, and failure handling that prevents error propagation cascades.

## Detailed Topics

### Why Multi-Agent Architectures

**The Context Bottleneck**
Reach for multi-agent architectures when a single agent's context fills with accumulated history, retrieved documents, and tool outputs to the point where performance degrades. Recognize three degradation signals: the lost-in-middle effect (attention weakens for mid-context content), attention scarcity (too many competing items), and context poisoning (irrelevant content displaces useful content).

Partition work across multiple context windows so each agent operates in a clean context focused on its subtask. Aggregate results at a coordination layer without any single context bearing the full burden.

**The Token Economics Reality**
Budget for substantially higher token costs. Production data shows multi-agent systems run at approximately 15x the token cost of a single-agent chat:

| Architecture | Token Multiplier | Use Case |
|--------------|------------------|----------|
| Single agent chat | 1x baseline | Simple queries |
| Single agent with tools | ~4x baseline | Tool-using tasks |
| Multi-agent system | ~15x baseline | Complex research/coordination |

Research on the BrowseComp evaluation found that three factors explain 95% of performance variance: token usage (80% of variance), number of tool calls, and model choice. This validates distributing work across agents with separate context windows to add capacity for parallel reasoning.

Prioritize model selection alongside architecture design — upgrading to better models often provides larger performance gains than doubling token budgets. BrowseComp data shows that model quality improvements frequently outperform raw token increases. Treat model selection and multi-agent architecture as complementary strategies.

**The Parallelization Argument**
Assign parallelizable subtasks to dedicated agents with fresh contexts rather than processing them sequentially in a single agent. A research task requiring searches across multiple independent sources, analysis of different documents, or comparison of competing approaches benefits from parallel execution. Total real-world time approaches the duration of the longest subtask rather than the sum of all subtasks.

**The Specialization Argument**
Configure each agent with only the system prompt, tools, and context it needs for its specific subtask. A general-purpose agent must carry all possible configurations in context, diluting attention. Specialized agents carry only what they need, operating with lean context optimized for their domain. Route from a coordinator to specialized agents to achieve specialization without combinatorial explosion.

### Architectural Patterns

**Pattern 1: Supervisor/Orchestrator**
Deploy a central agent that maintains global state and trajectory, decomposes user objectives into subtasks, and routes to appropriate workers.

```
User Query -> Supervisor -> [Specialist, Specialist, Specialist] -> Aggregation -> Final Output
```

Choose this pattern when: tasks have clear decomposition, coordination across domains is needed, or human oversight is important.

Expect these trade-offs: strict workflow control and easier human-in-the-loop interventions, but the supervisor context becomes a bottleneck, supervisor failures cascade to all workers, and the "telephone game" problem emerges where supervisors paraphrase sub-agent responses incorrectly.

**The Telephone Game Problem and Solution**
Anticipate that supervisor architectures initially perform approximately 50% worse than optimized versions due to the telephone game problem (LangGraph benchmarks). Supervisors paraphrase sub-agent responses, losing fidelity with each pass.

Fix this by implementing a `forward_message` tool that allows sub-agents to pass responses directly to users:

```python
def forward_message(message: str, to_user: bool = True):
    """
    Forward sub-agent response directly to user without supervisor synthesis.

    Use when:
    - Sub-agent response is final and complete
    - Supervisor synthesis would lose important details
    - Response format must be preserved exactly
    """
    if to_user:
        return {"type": "direct_response", "content": message}
    return {"type": "supervisor_input", "content": message}
```

Prefer swarm architectures over supervisors when sub-agents can respond directly to users, as this eliminates translation errors entirely.

**Pattern 2: Peer-to-Peer/Swarm**
Remove central control and allow agents to communicate directly based on predefined protocols. Any agent transfers control to any other through explicit handoff mechanisms.

```python
def transfer_to_agent_b():
    return agent_b  # Handoff via function return

agent_a = Agent(
    name="Agent A",
    functions=[transfer_to_agent_b]
)
```

Choose this pattern when: tasks require flexible exploration, rigid planning is counterproductive, or requirements emerge dynamically and defy upfront decomposition.

Expect these trade-offs: no single point of failure and effective breadth-first scaling, but coordination complexity increases with agent count, divergence risk rises without a central state keeper, and robust convergence constraints become essential.

Define explicit handoff protocols with state passing. Ensure agents communicate their context needs to receiving agents.

**Pattern 3: Hierarchical**
Organize agents into layers of abstraction: strategy (goal definition), planning (task decomposition), and execution (atomic tasks).

```
Strategy Layer (Goal Definition) -> Planning Layer (Task Decomposition) -> Execution Layer (Atomic Tasks)
```

Choose this pattern when: projects have clear hierarchical structure, workflows involve management layers, or tasks require both high-level planning and detailed execution.

Expect these trade-offs: clear separation of concerns and support for different context structures at different levels, but coordination overhead between layers,